Evaluating Noise Reduction Methods for Raman Spectroscopy in Transmission and Reflection Configurations
Bibliographic record
Abstract
This work involved comparing Raman signals obtained from two different Raman spectroscopy configurations, using two distinct noise reduction methods. The excitation light source was a laser diode with a wavelength of 532 nm. A long-pass filter and focusing lens were utilized to block the excited light from the source and concentrate the Raman signals due to their weaker nature compared to the excited light signals. Light of 532 nm wavelength was blocked during green laser diode illumination using a long-pass filter. Two configurations were studied: transmission Raman spectroscopy (TRS) and reflection Raman spectroscopy (RRS). Raman signals from both configurations were compared, and the boxcar averaging and Vancouver Raman algorithm (VRA) noise reduction methods were investigated and compared. The results showed that Raman signals from the transmission configuration were higher than those from the reflection configuration, and noise signals were effectively reduced using both the boxcar averaging and VRA methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".